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Introduction to Statistical Investigations 2nd ed. [köitmata]

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  • Formaat: Loose-leaf, 752 pages, kõrgus x laius x paksus: 282x216x31 mm, kaal: 1456 g, Contains 1 Loose-leaf
  • Ilmumisaeg: 27-Aug-2020
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 1119683459
  • ISBN-13: 9781119683452
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Introduction to Statistical Investigations 2nd ed.Suurem pilt
  • Formaat: Loose-leaf, 752 pages, kõrgus x laius x paksus: 282x216x31 mm, kaal: 1456 g, Contains 1 Loose-leaf
  • Ilmumisaeg: 27-Aug-2020
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 1119683459
  • ISBN-13: 9781119683452
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781119683452.html
Märksõnad:

Introduction to Statistical Investigations, Second Edition provides a unified framework for explaining variation across study designs and variable types, helping students increase their statistical literacy and appreciate the indispensable role of statistics in scientific research. Requiring only basic algebra as a prerequisite, the program uses the immersive, simulation-based inference approach for which the author team is known. Students engage with various aspects of data collection and analysis using real data and clear explanations designed to strengthen multivariable understanding and reinforce concepts.

Each chapter follows a coherent six-step statistical exploration and investigation method (ask a research question, design a study, explore the data, draw inferences, formulate conclusions, and look back and ahead) enabling students to assess a variety of concepts in a single assignment. Challenging questions based on research articles strengthen critical reading skills, fully worked examples demonstrate essential concepts and methods, and engaging visualizations illustrate key themes of explained variation. The end-of-chapter investigations expose students to various applications of statistics in the real world using real data from popular culture and published research studies in variety of disciplines. Accompanying examples throughout the text, user-friendly applets enable students to conduct the simulations and analyses covered in the book.

Preliminaries introduction to Statistical Investigations 1(29)
Section P.1 Introduction to the Six-Step Method
2(5)
Example P.1 Organ Donations
2(5)
Section P.2 Exploring Data
7(7)
Example P.2 Oh, Say Can You Sing?
7(7)
Section P.3 Exploring Random Processes
14(16)
Exploration P.3: Cars or Goats
14(16)
UNIT 1 Four Pillars of Inference: Strength, Size, Breadth, and Cause
30(248)
1 Significance: How Strong Is the Evidence?
31(86)
Section 1.1 Introduction to Chance Models
32(13)
Example 1.1 Can Dolphins Communicate?
33(8)
Exploration 1.1 Can Dogs Understand Human Cues?
41(4)
Section 1.2 Measuring the Strength of Evidence
45(12)
Example 1.2 Rock-Paper-Scissors
46(6)
Exploration 1.2 Tasting Water
52(5)
Section 1.3 Alternative Measure of Strength of Evidence
57(9)
Example 1.3 Heart Transplant Operations
58(4)
Exploration 1.3 Do People Use Facial Prototyping?
62(4)
Section 1.4 What Impacts Strength of Evidence?
66(9)
Example 1.4 Predicting Elections from Faces?
66(6)
Exploration 1.4 Competitive Advantage to Uniform Colors?
72(3)
Section 1.5 Inference for a Single Proportion: Theory-Based Approach
75(42)
Example 1.5 Halloween Treats
77(3)
Exploration 1.5 Eye Dominance
80(37)
2 Generalization: How Broadly Do the Results Apply?
117(71)
Section 2.1 Sampling from a Finite Population: Proportions
118(15)
Example 2.1 Voter Turnout
119(7)
Exploration 2.1 Sampling Words
126(7)
Section 2.2 Quantitative Data
133(10)
Example 2.2 Sampling Students
134(4)
Exploration 2.2 Sampling Words (cont.)
138(5)
Section 2.3 Theory-based Inference for a Population Mean
143(11)
Example 2.3 Estimating Elapsed Time
143(7)
Exploration 2.3 Sleepless Nights?
150(4)
Section 2.4 Other Statistics
154(34)
Example 2.4 Estimating Elapsed Time (cont.)
154(6)
Exploration 2.4 Backpack Weights
160(28)
3 Estimation: How Large Is the Effect? m
Section 3.1 Statistical Inference: Confidence Intervals
188(10)
Example 3.1 Can Dogs Sniff Out Cancer?
189(5)
Exploration 3.1 Kissing Right?
194(4)
Section 3.2 2SD and Theory-Based Confidence Intervals for a Single Proportion
198(9)
Example 3.2 Cyberbullying
198(5)
Exploration 3.2 How Mobile Are We?
203(4)
Section 3.3 2SD and Theory-Based Confidence Intervals for a Single Mean
207(6)
Example 3.3 Used Cars
207(3)
Exploration 3.3 Sleepless Nights? (cont.)
210(3)
Section 3.4 Factors That Affect the Width of a Confidence Interval
213(32)
Example 3.4 American Cat Ownership
214(2)
Exploration 3.4A Holiday Spending Habits
216(2)
Exploration 3.4B Reese's Pieces
218(27)
4 Causation: Can We Say What Caused the Effect?
245(33)
Section 4.1 Association and Confounding
246(6)
Example 4.1 Night Lights and Nearsightedness
247(3)
Exploration 4.1 Home Court Disadvantage?
250(2)
Section 4.2 Observational Studies Versus Experiments
252(26)
Example 4.2 Lying on the Internet
253(4)
Exploration 4.2 Have a Nice Trip
257(21)
UNIT 2 COMPARING TWO GROUPS
278(178)
5 Comparing Two Proportions
279(67)
Section 5.1 Comparing Two Groups: Categorical Response
280(8)
Example 5.1 Buckling Up?
280(5)
Exploration 5.1 Murderous Nurse?
285(3)
Section 5.2 Comparing Two Proportions: Simulation-Based Approach
288(16)
Example 5.2 Swimming with Dolphins
289(8)
Exploration 5.2 Is Yawning Contagious?
297(7)
Section 5.3 Comparing Two Proportions: Theory-Based Approach
304(42)
Example 5.3 Parents' Smoking Status and Their Babies' Sex
305(6)
Exploration 5.3 Donating Blood
311(35)
6 Comparing Two Means
346(61)
Section 6.1 Comparing Two Groups: Quantitative Response
347(7)
Example 6.1 Geyser Eruptions
347(3)
Exploration 6.1 Cancer Pamphlets
350(4)
Section 6.2 Comparing Two Means: Simulation-Based Approach
354(15)
Example 6.2 Dung Beetles
354(9)
Exploration 6.2 Lingering Effects of Sleep Deprivation
363(6)
Section 6.3 Comparing Two Means: Theory-Based Approach
369(38)
Example 6.3 Violent Video Games and Aggression
369(9)
Exploration 6.3 Close Friends
378(29)
7 Paired Data: One Quantitative Variable
407(49)
Section 7.1 Paired Designs
408(5)
Example 7.1 Can You Study with Music Blaring?
408(3)
Exploration 7.1 Rounding First Base
411(2)
Section 7.2 Simulation-Based Approach to Analyzing Paired Data
413(12)
Example 7.2 Rounding First Base (cont.)
414(6)
Exploration 7.2 Exercise and Heart Rate
420(5)
Section 7.3 Theory-Based Approach to Analyzing Data from Paired Samples
425(31)
Example 7.3 Dad Jokes?
425(6)
Exploration 7.3 Comparing Auction Formats
431(25)
UNIT 3 Analyzing More General Situations
456(2)
8 Comparing More Than Two Proportions
458(1)
Section 8.1 Comparing Multiple Proportions: Simulation-Based Approach
459(1)
Example 8.1 Coming to a Stop
460(6)
Exploration 8.1 Recruiting Organ Donors
466(4)
Section 8.2 Comparing Multiple Proportions: Theory-Based Approach
470(1)
Example 8.2 Sham Acupuncture
471(5)
Exploration 8.2A Conserving Hotel Towels
476(4)
Exploration 8.2B Nearsightedness and Night Lights Revisited
480(4)
Section 8.3 Chi-Square Goodness-of-Fit Test
484(35)
Example 8.3 Fair Die?
484(6)
Exploration 8.3 Are Birthdays Equally Distributed Throughout the Week?
490(29)
9 Comparing More Than Two Means
519(46)
Section 9.1 Comparing Multiple Means: Simulation-Based Approach
520(9)
Example 9.1 Comprehending Ambiguous Prose
520(5)
Exploration 9.1 Exercise and Brain Volume
525(4)
Section 9.2 Comparing Multiple Means: Theory-Based Approach
529(36)
Example 9.2 Recalling Ambiguous Prose
530(8)
Exploration 9.2 Comparing Popular Diets
538(27)
10 Two Quantitative Variables
565(1)
Section 10.1 Two Quantitative Variables: Scatterplots and Correlation
566(1)
Example 10.1 Why Whales Are Big, but Not Bigger
567(4)
Exploration 10.1 Height and Winning at Tennis
571(5)
Section 10.2 Inference for the Correlation Coefficient: Simulation-Based Approach
576(9)
Example 10.2 Exercise Intensity and Mood Changes
576(4)
Exploration 10.2 Draft Lottery
580(5)
Section 10.3 Least Squares Regression
585(11)
Example 10.3 Height and Winning at Tennis (cont.)
585(5)
Exploration 10.3 Predicting Height from Footprints
590(6)
Section 10.4 Inference for the Regression Slope: Simulation-Based Approach
596(5)
Example 10.4 Do Students Who Spend More Time in Non-Academic Activities Tend to Have Lower GPAs?
596(3)
Exploration 10.4 Predicting Brain Density from Number of Facebook Friends
599(2)
Section 10.5 Inference for the Regression Slope: Theory-Based Approach
601(1)
Example 10.5A Predicting Heart Rate from Body Temperature
602(4)
Example 10.5B Smoking and Drinking
606(2)
Exploration 10.5 Predicting Brain Density from Number of Facebook Friends (cont.)
608
UNIT 4 Probability (Online)
1(1)
11 Modeling Randomness
2(1)
Section 11.1 Basics of Probability
3(1)
Example 11.1 Random Ice Cream Prices
3(5)
Exploration 11.1 Random Babies
8(2)
Section 11.2 Probability Rules
10(9)
Example 11.2 Watching Films
11(4)
Exploration 11.2 Random Ice Cream Prices (cont.)
15(4)
Section 11.3 Conditional Probability and Independence
19(11)
Example 11.3 Watching Films Revisited
20(5)
Exploration 11.3A College Admissions
25(3)
Exploration 11.3B Rare Disease Testing
28(2)
Section 11.4 Discrete Random Variables
30(8)
Example 11.4 A Game of Chance
30(5)
Exploration 11.4 Traffic Lights
35(3)
Section 11.5 Random Variable Rules
38(12)
Example 11.5 A Game of Chance Revisited
38(7)
Exploration 11.5 Skee-Ball
45(5)
Section 11.6 Binomial and Geometric Random Variables
50(13)
Example 11.6 Time to Leave the Nest?
52(7)
Exploration 11.6 Clueless Quiz
59(4)
Section 11.7 Continuous Random Variables and Normal Distributions
63(9)
Example 11.7 Heights of Adult Women
65(4)
Exploration 11.7A Birthweights
69(2)
Exploration 11.7B Run, Girl, Run!
71(1)
Section 11.8 Revisiting Theory-Based Approximations of Sampling Distributions
72(573)
Example 11.8A Time to Leave the Nest Revisited
74(1)
Example 11.8B Intelligence Test
75(2)
Exploration 11.8A Racket Spinning
77(1)
Exploration 11.8B Random Ice Cream Prices (cont.)
77(568)
Appendix A Calculation Details 645(17)
Appendix B Stratified and Cluster Samples 662(4)
Solutions to selected exercises 666(62)
Index 728